```r
#install.packages('TDAmapper')
library(TDAmapper)
library(cluster)
library(vip)
## 
## Attaching package: 'vip'
## The following object is masked from 'package:utils':
## 
##     vi
#install.packages('kernlab’)
library(kernlab)
#install.packages(‘class’)
library(class)
#install.packages('nnet')
library(nnet)
#install.packages(‘randomForest’)
library(randomForest)
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
#install.packages('e1071')
library(e1071)                                                  
#install.packages("BayesFactor")
library(BayesFactor)
## Loading required package: coda
## 
## Attaching package: 'coda'
## The following object is masked from 'package:kernlab':
## 
##     nvar
## Loading required package: Matrix
## ************
## Welcome to BayesFactor 0.9.12-4.5. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
## 
## Type BFManual() to open the manual.
## ************
library(BayesPPD)
library(bayestestR)
#install.packages('igraph')
library('igraph')
## Warning: package 'igraph' was built under R version 4.3.3
## 
## Attaching package: 'igraph'
## The following object is masked from 'package:BayesFactor':
## 
##     compare
## The following object is masked from 'package:class':
## 
##     knn
## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum
## The following object is masked from 'package:base':
## 
##     union
#install.packages('locfit')
library(locfit)
## locfit 1.5-9.8    2023-06-11
#install.packages('ggplot2’)
library(ggplot2)
## 
## Attaching package: 'ggplot2'
## The following object is masked from 'package:randomForest':
## 
##     margin
## The following object is masked from 'package:kernlab':
## 
##     alpha
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:igraph':
## 
##     as_data_frame, groups, union
## The following object is masked from 'package:randomForest':
## 
##     combine
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
#install.packages('networkD3')
library(networkD3)
library(rstanarm)
## Loading required package: Rcpp
## This is rstanarm version 2.26.1
## - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
## - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
## - For execution on a local, multicore CPU with excess RAM we recommend calling
##   options(mc.cores = parallel::detectCores())
library(see)
#install.packages('tidyverse')
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.0
## ✔ readr     2.1.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ lubridate::%--%()       masks igraph::%--%()
## ✖ ggplot2::alpha()        masks kernlab::alpha()
## ✖ tibble::as_data_frame() masks dplyr::as_data_frame(), igraph::as_data_frame()
## ✖ dplyr::combine()        masks randomForest::combine()
## ✖ purrr::compose()        masks igraph::compose()
## ✖ purrr::cross()          masks kernlab::cross()
## ✖ tidyr::crossing()       masks igraph::crossing()
## ✖ tidyr::expand()         masks Matrix::expand()
## ✖ dplyr::filter()         masks stats::filter()
## ✖ dplyr::lag()            masks stats::lag()
## ✖ ggplot2::margin()       masks randomForest::margin()
## ✖ purrr::none()           masks locfit::none()
## ✖ tidyr::pack()           masks Matrix::pack()
## ✖ purrr::simplify()       masks igraph::simplify()
## ✖ tidyr::unpack()         masks Matrix::unpack()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
#install.packages('caret')
library(caret)
## Loading required package: lattice
## 
## Attaching package: 'caret'
## 
## The following object is masked from 'package:purrr':
## 
##     lift
## 
## The following objects are masked from 'package:rstanarm':
## 
##     compare_models, R2
#install.packages('ISLR')
library(ISLR)
#install.packages('MCMCpack')
library(MCMCpack)
## Loading required package: MASS
## 
## Attaching package: 'MASS'
## 
## The following object is masked from 'package:dplyr':
## 
##     select
## 
## ##
## ## Markov Chain Monte Carlo Package (MCMCpack)
## ## Copyright (C) 2003-2025 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
## ##
## ## Support provided by the U.S. National Science Foundation
## ## (Grants SES-0350646 and SES-0350613)
## ##
#linstall.packages("caret")
library(caret)
library(TDA)
## 
## Attaching package: 'TDA'
## 
## The following object is masked from 'package:cluster':
## 
##     silhouette
library(TDAstats)
library(ks)
## 
## Attaching package: 'ks'
## 
## The following object is masked from 'package:TDA':
## 
##     kde
## 
## The following object is masked from 'package:MCMCpack':
## 
##     vech
## 
## The following object is masked from 'package:igraph':
## 
##     compare
## 
## The following object is masked from 'package:BayesFactor':
## 
##     compare
#install.packages('MLmetrics')
library(MLmetrics)
## 
## Attaching package: 'MLmetrics'
## 
## The following objects are masked from 'package:caret':
## 
##     MAE, RMSE
## 
## The following object is masked from 'package:base':
## 
##     Recall
#install.packages('googledrive')
library(googledrive)
#install.packages('stringr')
library(stringr)
#install.packages('ks')
library(ks)
library(GGally)
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
##Add Bayesian tests functions

#create function to conduct the Bayesian Sign Test
BayesianSignTest <- function(diffVector,rope_min,rope_max) {

  library(MCMCpack)

  samples <- 3000

  #build the vector 0.5 1 1 ....... 1 

  weights <- c(0.5,rep(1,length(diffVector)))

  #add the fake first observation in 0

  diffVector <- c (0, diffVector)  


  #for the moment we implement the sign test. Signedrank will follows

  probLeft <- mean (diffVector < rope_min)

  probRope <- mean (diffVector > rope_min & diffVector < rope_max)

  probRight <- mean (diffVector > rope_max)

  results = list ("probLeft"=probLeft, "probRope"=probRope,
                  
                  "probRight"=probRight)
  
  return (results)
}


##Create function to conduct Bayesian Signed Rank Test

BayesianSignedRank <- function(diffVector,rope_min,rope_max) {
  
  library(MCMCpack)
  
  samples <- 30000
  
  #build the vector 0.5 1 1 ....... 1
  weights <- c(0.5,rep(1,length(diffVector)))
  
  #add the fake first observation in 0
  diffVector <- c (0, diffVector)
  
  sampledWeights <- rdirichlet(samples,weights)
  
  winLeft <- vector(length = samples)
  winRope <- vector(length = samples)
  winRight <- vector(length = samples)
  
  for (rep in 1:samples){
    currentWeights <- sampledWeights[rep,]
    for (i in 1:length(currentWeights)){
      for (j in 1:length(currentWeights)){
        product= currentWeights[i] * currentWeights[j]
        if (diffVector[i]+diffVector[j] > (2*rope_max) ) {
          winRight[rep] <- winRight[rep] + product
        }
        else if (diffVector[i]+diffVector[j] > (2*rope_min) ) {
          winRope[rep] <- winRope[rep] + product
        }
        else {
          winLeft[rep] <- winLeft[rep] + product
        }

      }
    }
    maxWins=max(winRight[rep],winRope[rep],winLeft[rep])
    winners = (winRight[rep]==maxWins)*1 + (winRope[rep]==maxWins)*1 + (winLeft[rep]==maxWins)*1
    winRight[rep] <- (winRight[rep]==maxWins)*1/winners
    winRope[rep] <- (winRope[rep]==maxWins)*1/winners
    winLeft[rep] <- (winLeft[rep]==maxWins)*1/winners
  }
  
  
  results = list ("winLeft"=mean(winLeft), "winRope"=mean(winRope),
                  "winRight"=mean(winRight) )
  return (results)
  
}


#Create function to conduct the Bayesian Correlated t.test

#diff_a_b is a vector of differences between the two classifiers, on each fold of cross-validation.
#If you have done 10 runs of 10-folds cross-validation, you have 100 results for each classifier.
#You should have run cross-validation on the same folds for the two classifiers.
#Then diff_a_b is the difference fold-by-fold.

#rho is the correlation of the cross-validation results: 1/(number of folds)
#rope_min and rope_max are the lower and the upper bound of the rope
 
correlatedBayesianTtest <- function(diff_a_b,rho,rope_min,rope_max){
   if (rope_max < rope_min){
     stop("rope_max should be larger than rope_min")
   }
     
  delta <- mean(diff_a_b)
  n <- length(diff_a_b)
  df <- n-1
  stdX <- sd(diff_a_b)
  sp <- sd(diff_a_b)*sqrt(1/n + rho/(1-rho))
  p.left <- pt((rope_min - delta)/sp, df)
  p.rope <- pt((rope_max - delta)/sp, df)-p.left
  results <- list('left'=p.left,'rope'=p.rope,'right'=1-p.left-p.rope)
  return (results)
}
set.seed(16974)
##Random Forest Results

rf_dataset_av<-c(0.8552, 0.9265562, 0.97876957)

rf_pca.5.50.5_n1_av<-c(0.9719, 0.9118504,0.99843287)
rf_pca.5.50.5_n2_av<-c(0.7323, 0.9020974,0.9847063)
rf_pca.5.50.5_n3_av<-c(0.8444, 0.93893757, 0.9847063)
rf_pca.5.50.5_n4_av<-c(0.9536, 0.97106917,0.9847063)
rf_pca.5.50.5_n5_av<-c(0.9983, 1,0.9847063)

rf_kde.5.50.5_n1_av<-c(0.8627, 0.951, 0.96649363)
rf_kde.5.50.5_n2_av<-c(0.8467, 0.944, 0.9786583)
rf_kde.5.50.5_n3_av<-c(0.8349, 0.913, 0.9840646)
rf_kde.5.50.5_n4_av<-c(0.8536, 0.820, 0.98788763)
rf_kde.5.50.5_n5_av<-c(0.8682, 0.729, 0.98885033)

   
########################   ROPE PCA

diff_rf_pca.5.50.5_n1_av<-rf_dataset_av - rf_pca.5.50.5_n1_av

bsr_diff_rf_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n1_av
## $winLeft
## [1] 0.6768
## 
## $winRope
## [1] 0.2408667
## 
## $winRight
## [1] 0.08233333
bsr_diff_rf_pca.5.50.5_n1_av_odds.left<-bsr_diff_rf_pca.5.50.5_n1_av $winLeft/bsr_diff_rf_pca.5.50.5_n1_av $winRight
bsr_diff_rf_pca.5.50.5_n1_av_odds.left
## [1] 8.220243
plot(rope(diff_rf_pca.5.50.5_n1_av,c(-0.01,0.01)))

diff_rf_pca.5.50.5_n2_av<-rf_dataset_av - rf_pca.5.50.5_n2_av

bsr_diff_rf_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n2_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.301
## 
## $winRight
## [1] 0.699
bsr_diff_rf_pca.5.50.5_n2_av_odds.left<-bsr_diff_rf_pca.5.50.5_n2_av $winLeft/bsr_diff_rf_pca.5.50.5_n2_av $winRight
bsr_diff_rf_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.50.5_n2_av,c(-0.01,0.01)))

diff_rf_pca.5.50.5_n3_av<-rf_dataset_av - rf_pca.5.50.5_n3_av

bsr_diff_rf_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n3_av
## $winLeft
## [1] 0.04913333
## 
## $winRope
## [1] 0.9011333
## 
## $winRight
## [1] 0.04973333
bsr_diff_rf_pca.5.50.5_n3_av_odds.left<-bsr_diff_rf_pca.5.50.5_n3_av $winLeft/bsr_diff_rf_pca.5.50.5_n3_av $winRight
bsr_diff_rf_pca.5.50.5_n3_av_odds.left
## [1] 0.9879357
plot(rope(diff_rf_pca.5.50.5_n3_av,c(-0.01,0.01)))

diff_rf_pca.5.50.5_n4_av<-rf_dataset_av - rf_pca.5.50.5_n4_av

bsr_diff_rf_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n4_av
## $winLeft
## [1] 0.8558667
## 
## $winRope
## [1] 0.1441333
## 
## $winRight
## [1] 0
bsr_diff_rf_pca.5.50.5_n4_av_odds.left<-bsr_diff_rf_pca.5.50.5_n4_av $winLeft/bsr_diff_rf_pca.5.50.5_n4_av $winRight
bsr_diff_rf_pca.5.50.5_n4_av_odds.left
## [1] Inf
plot(rope(diff_rf_pca.5.50.5_n4_av,c(-0.01,0.01)))

diff_rf_pca.5.50.5_n5_av<-rf_dataset_av - rf_pca.5.50.5_n5_av

bsr_diff_rf_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_rf_pca.5.50.5_n5_av
## $winLeft
## [1] 0.8575667
## 
## $winRope
## [1] 0.1424333
## 
## $winRight
## [1] 0
bsr_diff_rf_pca.5.50.5_n5_av_odds.left<-bsr_diff_rf_pca.5.50.5_n5_av $winLeft/bsr_diff_rf_pca.5.50.5_n5_av $winRight
bsr_diff_rf_pca.5.50.5_n5_av_odds.left
## [1] Inf
plot(rope(diff_rf_pca.5.50.5_n5_av,c(-0.01,0.01)))

##########################    ROPE KDE

diff_rf_kde.5.50.5_n1_av<-rf_dataset_av - rf_kde.5.50.5_n1_av

bsr_diff_rf_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n1_av
## $winLeft
## [1] 0.2614333
## 
## $winRope
## [1] 0.684
## 
## $winRight
## [1] 0.05456667
bsr_diff_rf_kde.5.50.5_n1_av_odds.left<-bsr_diff_rf_kde.5.50.5_n1_av$winLeft/bsr_diff_rf_kde.5.50.5_n1_av$winRight
bsr_diff_rf_kde.5.50.5_n1_av_odds.left
## [1] 4.791081
plot(rope(diff_rf_kde.5.50.5_n1_av,c(-0.01,0.01)))

diff_rf_kde.5.50.5_n2_av<-rf_dataset_av - rf_kde.5.50.5_n2_av

bsr_diff_rf_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n2_av
## $winLeft
## [1] 0.0473
## 
## $winRope
## [1] 0.9527
## 
## $winRight
## [1] 0
bsr_diff_rf_kde.5.50.5_n2_av_odds.left<-bsr_diff_rf_kde.5.50.5_n2_av$winLeft/bsr_diff_rf_kde.5.50.5_n2_av$winRight
bsr_diff_rf_kde.5.50.5_n2_av_odds.left
## [1] Inf
plot(rope(diff_rf_kde.5.50.5_n2_av,c(-0.01,0.01)))

diff_rf_kde.5.50.5_n3_av<-rf_dataset_av - rf_kde.5.50.5_n3_av

bsr_diff_rf_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n3_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.5823333
## 
## $winRight
## [1] 0.4176667
bsr_diff_rf_kde.5.50.5_n3_av_odds.left<-bsr_diff_rf_kde.5.50.5_n3_av$winLeft/bsr_diff_rf_kde.5.50.5_n3_av$winRight
bsr_diff_rf_kde.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_rf_kde.5.50.5_n3_av,c(-0.01,0.01)))

diff_rf_kde.5.50.5_n4_av<-rf_dataset_av - rf_kde.5.50.5_n4_av

bsr_diff_rf_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n4_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.5781333
## 
## $winRight
## [1] 0.4218667
bsr_diff_rf_kde.5.50.5_n4_av_odds.left<-bsr_diff_rf_kde.5.50.5_n4_av$winLeft/bsr_diff_rf_kde.5.50.5_n4_av$winRight
bsr_diff_rf_kde.5.50.5_n4_av_odds.left
## [1] 0
plot(rope(diff_rf_kde.5.50.5_n4_av,c(-0.01,0.01)))

diff_rf_kde.5.50.5_n5_av<-rf_dataset_av - rf_kde.5.50.5_n5_av

bsr_diff_rf_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_rf_kde.5.50.5_n5_av
## $winLeft
## [1] 0.3848667
## 
## $winRope
## [1] 0.1318667
## 
## $winRight
## [1] 0.4832667
bsr_diff_rf_kde.5.50.5_n5_av_odds.left<-bsr_diff_rf_kde.5.50.5_n5_av$winLeft/bsr_diff_rf_kde.5.50.5_n5_av$winRight
bsr_diff_rf_kde.5.50.5_n5_av_odds.left
## [1] 0.7963857
plot(rope(diff_rf_kde.5.50.5_n5_av,c(-0.01,0.01)))

################################  Support Vector Machine

##Support Vector Machine Results

svm_dataset_av<-c(0.8204, 0.929, 0.97677423)

svm_pca.5.50.5_n1_av<-c(0.6973, 0.915, 0.998432867)
svm_pca.5.50.5_n2_av<-c(0.6970, 0.645, 0.9837314)
svm_pca.5.50.5_n3_av<-c(0.8014, 0.947, 0.9199994)
svm_pca.5.50.5_n4_av<-c(0.9460, 0.981, 0.958260767)
svm_pca.5.50.5_n5_av<-c(0.9980, 1.000, 1.00)

svm_kde.5.50.5_n1_av<-c(0.8147, 0.955, 0.988035433)
svm_kde.5.50.5_n2_av<-c(0.8076, 0.947, 0.980024)
svm_kde.5.50.5_n3_av<-c(0.8026, 0.918, 0.985011133)
svm_kde.5.50.5_n4_av<-c(0.8388, 0.820, 0.988626333)
svm_kde.5.50.5_n5_av<-c(0.8051, 0.754, 0.989235033)

   
########################   ROPE PCA

diff_svm_pca.5.50.5_n1_av<-svm_dataset_av - svm_pca.5.50.5_n1_av

bsr_diff_svm_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n1_av
## $winLeft
## [1] 0.1495
## 
## $winRope
## [1] 0.1502333
## 
## $winRight
## [1] 0.7002667
bsr_diff_svm_pca.5.50.5_n1_av_odds.left<-bsr_diff_svm_pca.5.50.5_n1_av$winLeft/bsr_diff_svm_pca.5.50.5_n1_av $winRight
bsr_diff_svm_pca.5.50.5_n1_av_odds.left
## [1] 0.2134901
plot(rope(diff_svm_pca.5.50.5_n1_av,c(-0.01,0.01)))

diff_svm_pca.5.50.5_n2_av<-svm_dataset_av - svm_pca.5.50.5_n2_av

bsr_diff_svm_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n2_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1438
## 
## $winRight
## [1] 0.8562
bsr_diff_svm_pca.5.50.5_n2_av_odds.left<-bsr_diff_svm_pca.5.50.5_n2_av$winLeft/bsr_diff_svm_pca.5.50.5_n1_av$winRight
bsr_diff_svm_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.50.5_n2_av,c(-0.01,0.01)))

diff_svm_pca.5.50.5_n3_av<-svm_dataset_av - svm_pca.5.50.5_n3_av

bsr_diff_svm_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n3_av
## $winLeft
## [1] 0.07836667
## 
## $winRope
## [1] 0.2436667
## 
## $winRight
## [1] 0.6779667
bsr_diff_svm_pca.5.50.5_n3_av_odds.left<-bsr_diff_svm_pca.5.50.5_n3_av$winLeft/bsr_diff_svm_pca.5.50.5_n3_av$winRight
bsr_diff_svm_pca.5.50.5_n3_av_odds.left
## [1] 0.1155907
plot(rope(diff_svm_pca.5.50.5_n3_av,c(-0.01,0.01)))

diff_svm_pca.5.50.5_n4_av<-svm_dataset_av - svm_pca.5.50.5_n4_av

bsr_diff_svm_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n4_av
## $winLeft
## [1] 0.8869333
## 
## $winRope
## [1] 0.05153333
## 
## $winRight
## [1] 0.06153333
bsr_diff_svm_pca.5.50.5_n4_av_odds.left<-bsr_diff_svm_pca.5.50.5_n4_av$winLeft/bsr_diff_svm_pca.5.50.5_n4_av$winRight
bsr_diff_svm_pca.5.50.5_n4_av_odds.left
## [1] 14.41387
plot(rope(diff_svm_pca.5.50.5_n4_av,c(-0.01,0.01)))

diff_svm_pca.5.50.5_n5_av<-svm_dataset_av - svm_pca.5.50.5_n5_av

bsr_diff_svm_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_svm_pca.5.50.5_n5_av
## $winLeft
## [1] 0.9919
## 
## $winRope
## [1] 0.0081
## 
## $winRight
## [1] 0
bsr_diff_svm_pca.5.50.5_n5_av_odds.left<-bsr_diff_svm_pca.5.50.5_n5_av$winLeft/bsr_diff_svm_pca.5.50.5_n5_av$winRight
bsr_diff_svm_pca.5.50.5_n5_av_odds.left
## [1] Inf
plot(rope(diff_svm_pca.5.50.5_n5_av,c(-0.01,0.01)))

##########################    ROPE KDE

diff_svm_kde.5.50.5_n1_av<-svm_dataset_av - svm_kde.5.50.5_n1_av

bsr_diff_svm_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n1_av
## $winLeft
## [1] 0.6059667
## 
## $winRope
## [1] 0.3940333
## 
## $winRight
## [1] 0
bsr_diff_svm_kde.5.50.5_n1_av_odds.left<-bsr_diff_svm_kde.5.50.5_n1_av$winLeft/bsr_diff_svm_kde.5.50.5_n1_av$winRight
bsr_diff_svm_kde.5.50.5_n1_av_odds.left
## [1] Inf
plot(rope(diff_svm_kde.5.50.5_n1_av,c(-0.01,0.01)))

diff_svm_kde.5.50.5_n2_av<-svm_dataset_av - svm_kde.5.50.5_n2_av

bsr_diff_svm_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n2_av
## $winLeft
## [1] 0.1687667
## 
## $winRope
## [1] 0.7766333
## 
## $winRight
## [1] 0.0546
bsr_diff_svm_kde.5.50.5_n2_av_odds.left<-bsr_diff_svm_kde.5.50.5_n2_av$winLeft/bsr_diff_svm_kde.5.50.5_n2_av$winRight
bsr_diff_svm_kde.5.50.5_n2_av_odds.left
## [1] 3.090965
plot(rope(diff_svm_kde.5.50.5_n2_av,c(-0.01,0.01)))

diff_svm_kde.5.50.5_n3_av<-svm_dataset_av - svm_kde.5.50.5_n3_av

bsr_diff_svm_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n3_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.6716333
## 
## $winRight
## [1] 0.3283667
bsr_diff_svm_kde.5.50.5_n3_av_odds.left<-bsr_diff_svm_kde.5.50.5_n3_av$winLeft/bsr_diff_svm_kde.5.50.5_n3_av$winRight
bsr_diff_svm_kde.5.50.5_n3_av_odds.left
## [1] 0
plot(rope(diff_svm_kde.5.50.5_n3_av,c(-0.01,0.01)))

diff_svm_kde.5.50.5_n4_av<-svm_dataset_av - svm_kde.5.50.5_n4_av

bsr_diff_svm_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n4_av
## $winLeft
## [1] 0.3897
## 
## $winRope
## [1] 0.1306333
## 
## $winRight
## [1] 0.4796667
bsr_diff_svm_kde.5.50.5_n4_av_odds.left<-bsr_diff_svm_kde.5.50.5_n4_av$winLeft/bsr_diff_svm_kde.5.50.5_n4_av$winRight
bsr_diff_svm_kde.5.50.5_n4_av_odds.left
## [1] 0.8124392
plot(rope(diff_svm_kde.5.50.5_n4_av,c(-0.01,0.01)))

diff_svm_kde.5.50.5_n5_av<-svm_dataset_av - svm_kde.5.50.5_n5_av

bsr_diff_svm_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_svm_kde.5.50.5_n5_av
## $winLeft
## [1] 0.07643333
## 
## $winRope
## [1] 0.2448667
## 
## $winRight
## [1] 0.6787
bsr_diff_svm_kde.5.50.5_n5_av_odds.left<-bsr_diff_svm_kde.5.50.5_n5_av$winLeft/bsr_diff_svm_kde.5.50.5_n5_av$winRight
bsr_diff_svm_kde.5.50.5_n5_av_odds.left
## [1] 0.1126173
plot(rope(diff_svm_kde.5.50.5_n5_av,c(-0.01,0.01)))

#########################  Neural Network

##Neural Network Results

nn1_dataset_av<-c(0.8295538, 0.602, 0.97852987)

nn1_pca.5.50.5_n1_av<-c(0.97335787, 0.864, 0.99843287)
nn1_pca.5.50.5_n2_av<-c(0.69048193, 0.468, 0.98379247)
nn1_pca.5.50.5_n3_av<-c(0.82212793, 0.856, 0.911938)
nn1_pca.5.50.5_n4_av<-c(0.9531738, 0.862, 0.96000143)
nn1_pca.5.50.5_n5_av<-c(0.99798667, 0.998, 0.66823899)

nn1_kde.5.50.5_n1_av<-c(0.5557, 0.572, 0.967122867)
nn1_kde.5.50.5_n2_av<-c(0.8085, 0.746, 0.9856422)
nn1_kde.5.50.5_n3_av<-c(0.8093, 0.876, 0.985484367)
nn1_kde.5.50.5_n4_av<-c(0.8354, 0.788, 0.988035433)
nn1_kde.5.50.5_n5_av<-c(0.8686, 0.740, 0.989234933)

   
########################   ROPE PCA

diff_nn1_pca.5.50.5_n1_av<-nn1_dataset_av - nn1_pca.5.50.5_n1_av

bsr_diff_nn1_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n1_av
## $winLeft
## [1] 0.9632
## 
## $winRope
## [1] 0.0368
## 
## $winRight
## [1] 0
bsr_diff_nn1_pca.5.50.5_n1_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n1_av$winLeft/bsr_diff_nn1_pca.5.50.5_n1_av$winRight
bsr_diff_nn1_pca.5.50.5_n1_av_odds.left
## [1] Inf
plot(rope(diff_nn1_pca.5.50.5_n1_av,c(-0.01,0.01)))

diff_nn1_pca.5.50.5_n2_av<-nn1_dataset_av - nn1_pca.5.50.5_n2_av

bsr_diff_nn1_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n2_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1440333
## 
## $winRight
## [1] 0.8559667
bsr_diff_nn1_pca.5.50.5_n2_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n2_av$winLeft/bsr_diff_nn1_pca.5.50.5_n2_av$winRight
bsr_diff_nn1_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_nn1_pca.5.50.5_n2_av,c(-0.01,0.01)))

diff_nn1_pca.5.50.5_n3_av<-nn1_dataset_av - nn1_pca.5.50.5_n3_av

bsr_diff_nn1_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n3_av
## $winLeft
## [1] 0.4963667
## 
## $winRope
## [1] 0.1999667
## 
## $winRight
## [1] 0.3036667
bsr_diff_nn1_pca.5.50.5_n3_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n3_av$winLeft/bsr_diff_nn1_pca.5.50.5_n3_av$winRight
bsr_diff_nn1_pca.5.50.5_n3_av_odds.left
## [1] 1.634577
plot(rope(diff_nn1_pca.5.50.5_n3_av,c(-0.01,0.01)))

diff_nn1_pca.5.50.5_n4_av<-nn1_dataset_av - nn1_pca.5.50.5_n4_av

bsr_diff_nn1_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n4_av
## $winLeft
## [1] 0.8885
## 
## $winRope
## [1] 0.05096667
## 
## $winRight
## [1] 0.06053333
bsr_diff_nn1_pca.5.50.5_n4_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n4_av$winLeft/bsr_diff_nn1_pca.5.50.5_n4_av$winRight
bsr_diff_nn1_pca.5.50.5_n4_av_odds.left
## [1] 14.67786
plot(rope(diff_nn1_pca.5.50.5_n4_av,c(-0.01,0.01)))

diff_nn1_pca.5.50.5_n5_av<-nn1_dataset_av - nn1_pca.5.50.5_n5_av

bsr_diff_nn1_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_nn1_pca.5.50.5_n5_av
## $winLeft
## [1] 0.7272667
## 
## $winRope
## [1] 0.01633333
## 
## $winRight
## [1] 0.2564
bsr_diff_nn1_pca.5.50.5_n5_av_odds.left<-bsr_diff_nn1_pca.5.50.5_n5_av$winLeft/bsr_diff_nn1_pca.5.50.5_n5_av$winRight
bsr_diff_nn1_pca.5.50.5_n5_av_odds.left
## [1] 2.836453
plot(rope(diff_nn1_pca.5.50.5_n5_av,c(-0.01,0.01)))

##########################    ROPE KDE

diff_nn1_kde.5.50.5_n1_av<-nn1_dataset_av - nn1_kde.5.50.5_n1_av

bsr_diff_nn1_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n1_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.03696667
## 
## $winRight
## [1] 0.9630333
bsr_diff_nn1_kde.5.50.5_n1_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n1_av $winLeft/bsr_diff_nn1_kde.5.50.5_n1_av $winRight
bsr_diff_nn1_kde.5.50.5_n1_av_odds.left
## [1] 0
plot(rope(diff_nn1_kde.5.50.5_n1_av,c(-0.01,0.01)))

diff_nn1_kde.5.50.5_n2_av<-nn1_dataset_av - nn1_kde.5.50.5_n2_av

bsr_diff_nn1_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n2_av
## $winLeft
## [1] 0.4899
## 
## $winRope
## [1] 0.3676333
## 
## $winRight
## [1] 0.1424667
bsr_diff_nn1_kde.5.50.5_n2_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n2_av$winLeft/bsr_diff_nn1_kde.5.50.5_n2_av$winRight
bsr_diff_nn1_kde.5.50.5_n2_av_odds.left
## [1] 3.438699
plot(rope(diff_nn1_kde.5.50.5_n2_av,c(-0.01,0.01)))

diff_nn1_kde.5.50.5_n3_av<-nn1_dataset_av - nn1_kde.5.50.5_n3_av

bsr_diff_nn1_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n3_av
## $winLeft
## [1] 0.4949667
## 
## $winRope
## [1] 0.3646667
## 
## $winRight
## [1] 0.1403667
bsr_diff_nn1_kde.5.50.5_n3_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n3_av$winLeft/bsr_diff_nn1_kde.5.50.5_n3_av$winRight
bsr_diff_nn1_kde.5.50.5_n3_av_odds.left
## [1] 3.526241
plot(rope(diff_nn1_kde.5.50.5_n3_av,c(-0.01,0.01)))

diff_nn1_kde.5.50.5_n4_av<-nn1_dataset_av - nn1_kde.5.50.5_n4_av

bsr_diff_nn1_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n4_av
## $winLeft
## [1] 0.4213333
## 
## $winRope
## [1] 0.5786667
## 
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.50.5_n4_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n4_av$winLeft/bsr_diff_nn1_kde.5.50.5_n4_av$winRight
bsr_diff_nn1_kde.5.50.5_n4_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.50.5_n4_av,c(-0.01,0.01)))

diff_nn1_kde.5.50.5_n5_av<-nn1_dataset_av - nn1_kde.5.50.5_n5_av

bsr_diff_nn1_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_nn1_kde.5.50.5_n5_av
## $winLeft
## [1] 0.9623333
## 
## $winRope
## [1] 0.03766667
## 
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.50.5_n5_av_odds.left<-bsr_diff_nn1_kde.5.50.5_n5_av$winLeft/bsr_diff_nn1_kde.5.50.5_n5_av$winRight
bsr_diff_nn1_kde.5.50.5_n5_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.50.5_n5_av,c(-0.01,0.01)))

################################  Logistic Regression

##Logistic Regression Results

lr_dataset_av<-c(0.8486, 0.927, 0.97749227)

lr_pca.5.50.5_n1_av<-c(0.8954, 0.910, 0.99843287)
lr_pca.5.50.5_n2_av<-c(0.7171, 0.900, 0.9833048)
lr_pca.5.50.5_n3_av<-c(0.8289, 0.945, 0.89953653)
lr_pca.5.50.5_n4_av<-c(0.9485, 0.984, 0.9599969)
lr_pca.5.50.5_n5_av<-c(0.9338, 1.000, 0.66665922)

lr_kde.5.50.5_n1_av<-c(0.953, 0.572, 0.964291333)
lr_kde.5.50.5_n2_av<-c(0.947, 0.746, 0.980023833)
lr_kde.5.50.5_n3_av<-c(0.917, 0.876, 0.9862735)
lr_kde.5.50.5_n4_av<-c(0.827, 0.788, 0.9884785)
lr_kde.5.50.5_n5_av<-c(0.617, 0.740, 0.9890427)

   
########################   ROPE PCA

diff_lr_pca.5.50.5_n1_av<-lr_dataset_av - lr_pca.5.50.5_n1_av

bsr_diff_lr_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n1_av
## $winLeft
## [1] 0.7772667
## 
## $winRope
## [1] 0.1393667
## 
## $winRight
## [1] 0.08336667
bsr_diff_lr_pca.5.50.5_n1_av_odds.left<-bsr_diff_lr_pca.5.50.5_n1_av$winLeft/bsr_diff_lr_pca.5.50.5_n1_av$winRight
bsr_diff_lr_pca.5.50.5_n1_av_odds.left
## [1] 9.323471
plot(rope(diff_lr_pca.5.50.5_n1_av,c(-0.01,0.01)))

diff_lr_pca.5.50.5_n2_av<-lr_dataset_av - lr_pca.5.50.5_n2_av

bsr_diff_lr_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n2_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n2_av
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1459333
## 
## $winRight
## [1] 0.8540667
bsr_diff_lr_pca.5.50.5_n2_av_odds.left<-bsr_diff_lr_pca.5.50.5_n2_av$winLeft/bsr_diff_lr_pca.5.50.5_n2_av$winRight
bsr_diff_lr_pca.5.50.5_n2_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.50.5_n2_av,c(-0.01,0.01)))

diff_lr_pca.5.50.5_n3_av<-lr_dataset_av - lr_pca.5.50.5_n3_av

bsr_diff_lr_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n3_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n3_av
## $winLeft
## [1] 0.07876667
## 
## $winRope
## [1] 0.2441333
## 
## $winRight
## [1] 0.6771
bsr_diff_lr_pca.5.50.5_n3_av_odds.left<-bsr_diff_lr_pca.5.50.5_n3_av$winLeft/bsr_diff_lr_pca.5.50.5_n3_av$winRight
bsr_diff_lr_pca.5.50.5_n3_av_odds.left
## [1] 0.1163294
plot(rope(diff_lr_pca.5.50.5_n3_av,c(-0.01,0.01)))

diff_lr_pca.5.50.5_n4_av<-lr_dataset_av - lr_pca.5.50.5_n4_av

bsr_diff_lr_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n4_av
## $winLeft
## [1] 0.8857
## 
## $winRope
## [1] 0.0525
## 
## $winRight
## [1] 0.0618
bsr_diff_lr_pca.5.50.5_n4_av_odds.left<-bsr_diff_lr_pca.5.50.5_n4_av$winLeft/bsr_diff_lr_pca.5.50.5_n4_av$winRight
bsr_diff_lr_pca.5.50.5_n4_av_odds.left
## [1] 14.33172
plot(rope(diff_lr_pca.5.50.5_n4_av,c(-0.01,0.01)))

diff_lr_pca.5.50.5_n5_av<-lr_dataset_av - lr_pca.5.50.5_n5_av

bsr_diff_lr_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.50.5_n5_av),-0.01,0.01)
bsr_diff_lr_pca.5.50.5_n5_av
## $winLeft
## [1] 0.5425333
## 
## $winRope
## [1] 0.0159
## 
## $winRight
## [1] 0.4415667
bsr_diff_lr_pca.5.50.5_n5_av_odds.left<-bsr_diff_lr_pca.5.50.5_n5_av$winLeft/bsr_diff_lr_pca.5.50.5_n5_av$winRight
bsr_diff_lr_pca.5.50.5_n5_av_odds.left
## [1] 1.228656
plot(rope(diff_lr_pca.5.50.5_n5_av,c(-0.01,0.01)))

##########################    ROPE KDE

diff_lr_kde.5.50.5_n1_av<-lr_dataset_av - lr_kde.5.50.5_n1_av

bsr_diff_lr_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n1_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n1_av
## $winLeft
## [1] 0.2802
## 
## $winRope
## [1] 0.05953333
## 
## $winRight
## [1] 0.6602667
bsr_diff_lr_kde.5.50.5_n1_av_odds.left<-bsr_diff_lr_kde.5.50.5_n1_av $winLeft/bsr_diff_lr_kde.5.50.5_n1_av$winRight
bsr_diff_lr_kde.5.50.5_n1_av_odds.left
## [1] 0.424374
plot(rope(diff_lr_kde.5.50.5_n1_av,c(-0.01,0.01)))

diff_lr_kde.5.50.5_n2_av<-lr_dataset_av - lr_kde.5.50.5_n2_av

bsr_diff_lr_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n2_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n2_av
## $winLeft
## [1] 0.3011
## 
## $winRope
## [1] 0.2008333
## 
## $winRight
## [1] 0.4980667
bsr_diff_lr_kde.5.50.5_n2_av_odds.left<-bsr_diff_lr_kde.5.50.5_n2_av $winLeft/bsr_diff_lr_kde.5.50.5_n2_av$winRight
bsr_diff_lr_kde.5.50.5_n2_av_odds.left
## [1] 0.6045375
plot(rope(diff_lr_kde.5.50.5_n2_av,c(-0.01,0.01)))

diff_lr_kde.5.50.5_n3_av<-lr_dataset_av - lr_kde.5.50.5_n3_av

bsr_diff_lr_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n3_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n3_av
## $winLeft
## [1] 0.3464333
## 
## $winRope
## [1] 0.3018
## 
## $winRight
## [1] 0.3517667
bsr_diff_lr_kde.5.50.5_n3_av_odds.left<-bsr_diff_lr_kde.5.50.5_n3_av $winLeft/bsr_diff_lr_kde.5.50.5_n3_av$winRight
bsr_diff_lr_kde.5.50.5_n3_av_odds.left
## [1] 0.9848384
plot(rope(diff_lr_kde.5.50.5_n3_av,c(-0.01,0.01)))

diff_lr_kde.5.50.5_n4_av<-lr_dataset_av - lr_kde.5.50.5_n4_av

bsr_diff_lr_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n4_av
## $winLeft
## [1] 0.0794
## 
## $winRope
## [1] 0.1433333
## 
## $winRight
## [1] 0.7772667
bsr_diff_lr_kde.5.50.5_n4_av_odds.left<-bsr_diff_lr_kde.5.50.5_n4_av $winLeft/bsr_diff_lr_kde.5.50.5_n4_av$winRight
bsr_diff_lr_kde.5.50.5_n4_av_odds.left
## [1] 0.1021528
plot(rope(diff_lr_kde.5.50.5_n4_av,c(-0.01,0.01)))

diff_lr_kde.5.50.5_n5_av<-lr_dataset_av - lr_kde.5.50.5_n5_av

bsr_diff_lr_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.50.5_n5_av),-0.01,0.01)
bsr_diff_lr_kde.5.50.5_n5_av
## $winLeft
## [1] 0.0634
## 
## $winRope
## [1] 0.0513
## 
## $winRight
## [1] 0.8853
bsr_diff_lr_kde.5.50.5_n5_av_odds.left<-bsr_diff_lr_kde.5.50.5_n5_av $winLeft/bsr_diff_lr_kde.5.50.5_n5_av$winRight
bsr_diff_lr_kde.5.50.5_n5_av_odds.left
## [1] 0.07161414
plot(rope(diff_lr_kde.5.50.5_n5_av,c(-0.01,0.01)))

####################################################   Naive Bayes

##Naive Bayes Results

nb_dataset_av<-c(0.76203217, 0.90190013, 0.9592941)

nb_pca.5.50.5_n1_av<-c(0.97335777, 0.85852737, 0.9784864)
#nb_pca.5.50.5_n2_av<-c(0.5561222, NA, 0.95338763)
#nb_pca.5.50.5_n3_av<-c(0.7714502, NA, 0.89953653)
nb_pca.5.50.5_n4_av<-c(0.95786153, 0.984, 0.8939295)
#nb_pca.5.50.5_n5_av<-c(0.99798667, NA, NA)

#nb_kde.5.50.5_n1_av<-c(0.74527557, NA, 0.94903253)
#nb_kde.5.50.5_n2_av<-c(0.58463283, NA, 0.9538141)
#nb_kde.5.50.5_n3_av<-c(0.7714502, NA, 0.86790697)
nb_kde.5.50.5_n4_av<-c(0.9449102, 0.9565835, 0.8965201)
#nb_kde.5.50.5_n5_av<-c(0.99798667, NA, NA)


########################   ROPE PCA

diff_nb_pca.5.50.5_n1_av<-nb_dataset_av - nb_pca.5.50.5_n1_av

bsr_diff_nb_pca.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n1_av),-0.01,0.01)
bsr_diff_nb_pca.5.50.5_n1_av
## $winLeft
## [1] 0.6588667
## 
## $winRope
## [1] 0.0618
## 
## $winRight
## [1] 0.2793333
bsr_diff_nb_pca.5.50.5_n1_av_odds.left<-bsr_diff_nb_pca.5.50.5_n1_av$winLeft/bsr_diff_nb_pca.5.50.5_n1_av$winRight
bsr_diff_nb_pca.5.50.5_n1_av_odds.left
## [1] 2.358711
plot(rope(diff_nb_pca.5.50.5_n1_av,c(-0.01,0.01)))

#diff_nb_pca.5.50.5_n2_av<-nb_dataset_av - nb_pca.5.50.5_n2_av

#bsr_diff_nb_pca.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n2_av),-0.01,0.01)
#bsr_diff_nb_pca.5.50.5_n2_av

#bsr_diff_nb_pca.5.50.5_n2_av_odds.left<-bsr_diff_nb_pca.5.50.5_n2_av$winLeft/bsr_diff_nb_pca.5.50.5_n2_av$winRight
#bsr_diff_nb_pca.5.50.5_n2_av_odds.left

#plot(rope(diff_nb_pca.5.50.5_n2_av,c(-0.01,0.01)))


#diff_nb_pca.5.50.5_n3_av<-nb_dataset_av - nb_pca.5.50.5_n3_av

#bsr_diff_nb_pca.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n3_av),-0.01,0.01)
#bsr_diff_nb_pca.5.50.5_n3_av

#bsr_diff_nb_pca.5.50.5_n3_av_odds.left<-bsr_diff_nb_pca.5.50.5_n3_av$winLeft/bsr_diff_nb_pca.5.50.5_n3_av$winRight
#bsr_diff_nb_pca.5.50.5_n3_av_odds.left

#plot(rope(diff_nb_pca.5.50.5_n3_av,c(-0.01,0.01)))


diff_nb_pca.5.50.5_n4_av<-nb_dataset_av - nb_pca.5.50.5_n4_av

bsr_diff_nb_pca.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nb_pca.5.50.5_n4_av
## $winLeft
## [1] 0.8
## 
## $winRope
## [1] 0.0461
## 
## $winRight
## [1] 0.1539
bsr_diff_nb_pca.5.50.5_n4_av_odds.left<-bsr_diff_nb_pca.5.50.5_n4_av$winLeft/bsr_diff_nb_pca.5.50.5_n4_av$winRight
bsr_diff_nb_pca.5.50.5_n4_av_odds.left
## [1] 5.198181
plot(rope(diff_nb_pca.5.50.5_n4_av,c(-0.01,0.01)))

#diff_nb_pca.5.50.5_n5_av<-nb_dataset_av - nb_pca.5.50.5_n5_av

#bsr_diff_nb_pca.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.50.5_n5_av),-0.01,0.01)
#bsr_diff_nb_pca.5.50.5_n5_av

#bsr_diff_nb_pca.5.50.5_n5_av_odds.left<-bsr_diff_nb_pca.5.50.5_n5_av$winLeft/bsr_diff_nb_pca.5.50.5_n5_av$winRight
#bsr_diff_nb_pca.5.50.5_n5_av_odds.left

#plot(rope(diff_nb_pca.5.50.5_n5_av,c(-0.01,0.01)))


##########################    ROPE KDE

#diff_nb_kde.5.50.5_n1_av<-nb_dataset_av - nb_kde.5.50.5_n1_av

#bsr_diff_nb_kde.5.50.5_n1_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n1_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n1_av

#bsr_diff_nb_kde.5.50.5_n1_av_odds.left<-bsr_diff_nb_kde.5.50.5_n1_av $winLeft/bsr_diff_nb_kde.5.50.5_n1_av$winRight
#bsr_diff_nb_kde.5.50.5_n1_av_odds.left

#plot(rope(diff_nb_kde.5.50.5_n1_av,c(-0.01,0.01)))

#diff_nb_kde.5.50.5_n2_av<-nb_dataset_av - nb_kde.5.50.5_n2_av

#bsr_diff_nb_kde.5.50.5_n2_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n2_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n2_av

#bsr_diff_nb_kde.5.50.5_n2_av_odds.left<-bsr_diff_nb_kde.5.50.5_n2_av $winLeft/bsr_diff_nb_kde.5.50.5_n2_av$winRight
#bsr_diff_nb_kde.5.50.5_n2_av_odds.left

#plot(rope(diff_nb_kde.5.50.5_n2_av,c(-0.01,0.01)))


#diff_nb_kde.5.50.5_n3_av<-nb_dataset_av - nb_kde.5.50.5_n3_av

#bsr_diff_nb_kde.5.50.5_n3_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n3_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n3_av

#bsr_diff_nb_kde.5.50.5_n3_av_odds.left<-bsr_diff_nb_kde.5.50.5_n3_av $winLeft/bsr_diff_nb_kde.5.50.5_n3_av$winRight
#bsr_diff_nb_kde.5.50.5_n3_av_odds.left

#plot(rope(diff_nb_kde.5.50.5_n3_av,c(-0.01,0.01)))


diff_nb_kde.5.50.5_n4_av<-nb_dataset_av - nb_kde.5.50.5_n4_av

bsr_diff_nb_kde.5.50.5_n4_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n4_av),-0.01,0.01)
bsr_diff_nb_kde.5.50.5_n4_av
## $winLeft
## [1] 0.8018
## 
## $winRope
## [1] 0.04636667
## 
## $winRight
## [1] 0.1518333
bsr_diff_nb_kde.5.50.5_n4_av_odds.left<-bsr_diff_nb_kde.5.50.5_n4_av $winLeft/bsr_diff_nb_kde.5.50.5_n4_av$winRight
bsr_diff_nb_kde.5.50.5_n4_av_odds.left
## [1] 5.28079
plot(rope(diff_nb_kde.5.50.5_n4_av,c(-0.01,0.01)))

#diff_nb_kde.5.50.5_n5_av<-nb_dataset_av - nb_kde.5.50.5_n5_av

#bsr_diff_nb_kde.5.50.5_n5_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.50.5_n5_av),-0.01,0.01)
#bsr_diff_nb_kde.5.50.5_n5_av

#bsr_diff_nb_kde.5.50.5_n5_av_odds.left<-bsr_diff_nb_kde.5.50.5_n5_av $winLeft/bsr_diff_nb_kde.5.50.5_n5_av$winRight
#bsr_diff_nb_kde.5.50.5_n5_av_odds.left

#plot(rope(diff_nb_kde.5.50.5_n5_av,c(-0.01,0.01)))